These data are born digital, which allows researchers to analyze the brain images further by using advanced mathematical and statistical methods such as shape quantification or multivariate analysis.
Most of the genes known to control these processes during brain development, maturation and aging are highly conserved (Holland, 2003), though some show polymorphisms (cf.
Shape feature comparisons form the basis of Linnaean taxonomy, and even in cases of convergent evolution or brain disorders, they still provide a wealth of information about the nature of the processes involved.
MR images are generated by a complex interaction between static and dynamic electromagnetic fields and the tissue of interest, namely the brain that is encapsulated in the head of the subject.
Since VBM is available for many of the major neuroimaging software packages (e.g. FSL and SPM), it provides an efficient tool to test or generate specific hypotheses about brain changes over time.
It is noteworthy, that unlike DBM, considerable criticism and words of caution regarding the correct interpretation of VBM results has been leveled by the medical image computing community [2][3] In DBM, highly non-linear registration algorithms are used, and the statistical analyses are not performed on the registered voxels but on the deformation fields used to register them [4] (which requires multivariate approaches) or derived scalar properties thereof, which allows for univariate approaches [5].
Of course, multiple solutions exist for such non-linear warping procedures, and to balance appropriately between the potentially opposing requirements for global and local shape fit, ever more sophisticated registration algorithms are being developed.
However, due to the vast variety of registration algorithms, no widely accepted standard for DBM exists, which also prevented its incorporation into major neuroimaging software packages.
Diffeomorphometry[7] is the focus on comparison of shapes and forms with a metric structure based on diffeomorphisms, and is central to the field of computational anatomy.
[8] Diffeomorphic registration,[9] introduced in the 90's, is now an important player that uses computational procedures for constructing correspondences between coordinate systems based on sparse features and dense images, such as ANTS,[10] DARTEL,[11] DEMONS,[12] LDDMM,[13] or StationaryLDDMM.
The above-described morphometric methods provide the means to analyze such changes quantitatively, and MR imaging has been applied to ever more brain populations relevant to these time scales, both within humans and across species.
Beyond preterms, there have been a number of large-scale longitudinal MR-morphometric studies (often combined with cross-sectional approaches and other neuroimaging modalities) of normal brain development in humans.
[20] In order to interpret these findings, cellular processes have to be taken into consideration, especially those governing the pruning of axons, dendrites and synapses until an adult pattern of whole-brain connectivity is achieved (which can best be monitored using diffusion-weighted techniques).
Perhaps the most profound impact to date of brain morphometry on our understanding of the relationships between brain structure and function has been provided by a series of VBM studies targeted at proficiency in various performances: Licensed taxicab drivers in London were found to exhibit bilaterally increased gray matter volume in the posterior part of the hippocampus, both relative to controls from the general population[21] and to London bus drivers matched for driving experience and stress levels.
Similarly, gray matter changes were also found to correlate with professional experience in musicians, mathematicians and meditators, and with second language proficiency.
What is more, bilateral gray matter changes in the posterior and lateral parietal cortex of medical students memorizing for an intermediate exam could be detected over a period of just three months.
These studies of professional training inspired questions about the limits of MR-based morphometry in terms of time periods over which structural brain changes can be detected.
Given the time constraints under such conditions, brain morphometry is rarely involved in diagnostics but rather used for progress monitoring over periods of weeks and months and longer.
One study found that juggling novices showed a bilateral gray matter expansion in the medial temporal visual area (also known as V5) over a three-month period during which they had learned to sustain a three-ball cascade for at least a minute.
Whereas the former two studies involved students in their early twenties, the experiments were recently repeated with an elderly cohort, revealing the same kind of structural changes, although attenuated by lower juggling performance of this group.
[22] Using a completely different kind of intervention—application of Transcranial Magnetic Stimulation in daily sessions over five days—changes were observed in and near the TMS target areas as well as in the basal ganglia of volunteers in their mid-twenties, compared to a control group that had received placebo.
Even larger time gaps can be bridged by comparing human populations with a sufficiently long history of genetic separation, such as Central Europeans and Japanese.
Postmortem samples of living or recently extinct species, on the other hand, generally allow to obtain MR image qualities sufficient for morphometric analyses, though preservation artifacts would have to be taken into account.